Defects Identification of Partial Discharge Map in GIS

With GIS being widely used, partial discharge detecting and defect pattern recognition become more and more meaningful and important. To realize defects identification of partial discharge map in GIS, a novel method based on Radical Basis Function (RBF) neural network is proposed. Firstly, a model is constructed to simulate the discharge pattern map by the use of random function randint. Secondly, based on the model above, a lot of data which meet the condition can be collected to provide for pattern recognition. Then, a RBF network is introduced to identify the pattern recognition. It can be trained by using the data above. Finally, through changing training error, high correct rate can be got. These indicate that the method is effective.